| import torch | |
| import torch.nn as nn | |
| from .. import SparseTensor | |
| class SparseConv3d(nn.Module): | |
| def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None): | |
| super(SparseConv3d, self).__init__() | |
| if 'torchsparse' not in globals(): | |
| import torchsparse | |
| self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias) | |
| def forward(self, x: SparseTensor) -> SparseTensor: | |
| out = self.conv(x.data) | |
| new_shape = [x.shape[0], self.conv.out_channels] | |
| out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None) | |
| out._spatial_cache = x._spatial_cache | |
| out._scale = tuple([s * stride for s, stride in zip(x._scale, self.conv.stride)]) | |
| return out | |
| class SparseInverseConv3d(nn.Module): | |
| def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None): | |
| super(SparseInverseConv3d, self).__init__() | |
| if 'torchsparse' not in globals(): | |
| import torchsparse | |
| self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias, transposed=True) | |
| def forward(self, x: SparseTensor) -> SparseTensor: | |
| out = self.conv(x.data) | |
| new_shape = [x.shape[0], self.conv.out_channels] | |
| out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None) | |
| out._spatial_cache = x._spatial_cache | |
| out._scale = tuple([s // stride for s, stride in zip(x._scale, self.conv.stride)]) | |
| return out | |